Enhanced stochastic mobility prediction with multi-output Gaussian processes

Lui, ST; Peynot, T; Fitch, R; Sukkarieh, S

Enhanced stochastic mobility prediction with multi-output Gaussian processes

Lui, ST Peynot, T Fitch, R

Sukkarieh, S

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Publication Type:: Chapter
Citation:: 2015, 302 pp. 173 - 190
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Lui, ST	en_US
dc.contributor.author	Peynot, T	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Sukkarieh, S	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	2015, 302 pp. 173 - 190	en_US
dc.identifier.isbn	9783319083377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/103171
dc.description.abstract	© Springer International Publishing Switzerland 2016. Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.	en_US
dc.relation.isbasedon	10.1007/978-3-319-08338-4_14	en_US
dc.title	Enhanced stochastic mobility prediction with multi-output Gaussian processes	en_US
dc.type	Chapter
utslib.citation.volume	302	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	091303 Autonomous Vehicles	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	302	en_US

Abstract:

© Springer International Publishing Switzerland 2016. Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

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http://hdl.handle.net/10453/103171